A Comparative Study of the Spectral Conjugate Gradient Methods in Regression Analysis

In this paper, a spectral conjugate gradient (CG) method is introduced for solving the unconstrained optimization problems. This method are compared with others spectral CG coefficients. Several different dimensions for eighteen types of optimization problems are used to test the efficiency and robustness of spectral conjugate gradient methods by using Matlab subroutine programming. The method should possess the convergence analysis under strong Wolfe line search. The numerical results based on iteration number and CPU time are interpreted into performance profile by using SigmaPlot. This method will be implemented in regression analysis to validate its capability on estimating the data.